T
Tallulah S. Andrews
Researcher at Wellcome Trust Sanger Institute
Publications - 29
Citations - 3975
Tallulah S. Andrews is an academic researcher from Wellcome Trust Sanger Institute. The author has contributed to research in topics: Feature selection & Cluster analysis. The author has an hindex of 16, co-authored 26 publications receiving 2425 citations. Previous affiliations of Tallulah S. Andrews include Wellcome Trust Centre for Human Genetics & University of Oxford.
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Journal ArticleDOI
SC3: consensus clustering of single-cell RNA-seq data
Vladimir Yu. Kiselev,Kristina Kirschner,Michael T. Schaub,Michael T. Schaub,Tallulah S. Andrews,Andrew Yiu,Tamir Chandra,Tamir Chandra,Kedar Nath Natarajan,Kedar Nath Natarajan,Wolf Reik,Wolf Reik,Wolf Reik,Mauricio Barahona,Anthony R. Green,Martin Hemberg +15 more
TL;DR: It is demonstrated that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients and achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach.
Journal ArticleDOI
Challenges in unsupervised clustering of single-cell RNA-seq data.
TL;DR: This Review discusses the multiple algorithmic options for clustering scRNA-seq data, including various technical, biological and computational considerations.
Journal ArticleDOI
EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data
Aaron T. L. Lun,Samantha J. Riesenfeld,Tallulah S. Andrews,Tomás Gomes,John C. Marioni,John C. Marioni,John C. Marioni +6 more
TL;DR: This work describes a new statistical method, EmptyDrops, based on detecting significant deviations from the expression profile of the ambient solution that retains distinct cell types that would have been discarded by existing methods in several real data sets.
Posted ContentDOI
SC3 consensus clustering of singlecell RNASeq data
Vladimir Yu. Kiselev,Kristina Kirschner,Michael T. Schaub,Tallulah S. Andrews,Tamir Chandra,Kedar Nath Natarajan,Wolf Reik,Mauricio Barahona,Anthony R. Green,Martin Hemberg +9 more
TL;DR: Single-Cell Consensus Clustering (SC3), a tool for unsupervised clustering of scRNA-seq data, achieves high accuracy and robustness by consistently integrating different clustering solutions through a consensus approach.
Journal ArticleDOI
Identifying cell populations with scRNASeq
TL;DR: An overview of different experimental protocols and the most popular methods for facilitating the computational analysis of single-cell RNASeq focuses on approaches for identifying biologically important genes, projecting data into lower dimensions and clustering data into putative cell-populations.